Research map / Optimization / Implicit bias and generalization
Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the siamese network with a triplet network | Local image descriptor learning on UBC · UBC benchmark | FPR95 | Helped |
| Add the global loss to the triplet loss | TNet · UBC benchmark | FPR95 | Helped |
| Train the central-surround siamese network with global loss | Central-surround siamese network · UBC benchmark | FPR95 | Helped |
| Use global loss alone instead of combining it with triplet loss (ablation) | Triplet network · UBC benchmark | FPR95 | No clear effect |
Closest papers by meaning
- Learning Deep Descriptors With Scale-Aware Triplet Networks (CVPR 2018 · similarity 0.89)
- HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors (CVPR 2017 · similarity 0.86)
- Exploring Simple Siamese Representation Learning (CVPR 2021 · similarity 0.86)
- HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss (NeurIPS 2020 · similarity 0.86)
- Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning (CVPR 2019 · similarity 0.85)
- ConvNeXt V2: Co-Designing and Scaling ConvNets With Masked Autoencoders (CVPR 2023 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.